A pilot study to integrate HIV drug resistance gold standard interpretation algorithms using neural networks

dc.contributor.author	Singh, Y.
dc.contributor.author	Mars, M.
dc.contributor.other	Central University of Technology, Free State, Bloemfontein
dc.date.accessioned	2015-10-05T10:26:53Z
dc.date.available	2015-10-05T10:26:53Z
dc.date.issued	2013
dc.date.issued	2013
dc.description	Published Article	en_US
dc.description.abstract	There are several HIV drug resistant interpretation algorithms which produce different resistance measures even if applied to the same resistance profile. This discrepancy leads to confusion in the mind of the physician when choosing the best ARV therapy.	en_US
dc.format.extent	279 496 bytes, 1 file
dc.format.mimetype	Application/PDF
dc.identifier.issn	16844998
dc.identifier.uri	http://hdl.handle.net/11462/639
dc.language.iso	en_US	en_US
dc.publisher	Journal for New Generation Sciences, Vol 11, Issue 2: Central University of Technology, Free State, Bloemfontein
dc.relation.ispartofseries	Journal for New Generation Sciences;Vol 11, Issue 2
dc.rights.holder	Central University of Technology, Free State, Bloemfontein
dc.subject	Machine learning	en_US
dc.subject	Artificial intelligence	en_US
dc.subject	Neural networks	en_US
dc.subject	HIV drug resistance	en_US
dc.title	A pilot study to integrate HIV drug resistance gold standard interpretation algorithms using neural networks	en_US
dc.type	Article	en_US

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